Most schools are still arguing about whether students should be allowed to touch AI. We think that argument is already over. The tools are in every pocket, woven into search engines, homework apps, and writing software your child already uses. Pretending otherwise doesn’t protect anyone — it just leaves students to figure it out alone, in secret, with no one teaching them the difference between using a tool well and laundering its output as their own work.
So our posture is simple, and it has two halves. First, we teach AI literacy — how to prompt, how to interrogate, how to catch the machine when it lies. Second, we assess in ways AI can’t fake. A student demonstrates understanding out loud, at a dissection tray, defending a real lab notebook and identifying structures on a specimen in front of a person. There is no prompt that opens a specimen and finds the structure for you. When the assessment is honest, the studying becomes honest too, and AI turns back into what it should have been all along: a tutor that never gets tired, not a ghostwriter.
The course’s AI posture
We treat AI the way a careful anatomy teacher treats a scalpel. It is genuinely useful and genuinely capable of doing damage, and the answer to both facts is the same: instruction, not prohibition. A student who has never been taught how AI fails — how it invents citations, mislabels a structure with total confidence, names an organ that isn’t in that species, and tells you what you want to hear — is far more dangerous to their own learning than one who has been shown exactly where the tool breaks.
Our aim is a student who can sit down with an AI assistant and treat it like a sharp, fast, slightly unreliable study partner: useful for drilling the names of anatomical structures, useful for re-explaining homology versus analogy three ways, never trusted on an identification without checking the specimen, and never — not once — allowed to stand in for the observation the student is supposed to be doing. The line we draw is not about the tool. It is about whose understanding ends up in the work.
Encouraged vs. off-limits
The examples below distinguish help with studying from replacing the student’s work. The teacher should explain what assistance is permitted for each task. Students should be able to describe how they used a tool and check its suggestions.
| ✓ Encouraged | ✗ Off-limits |
|---|---|
| Drilling facts you must know cold — ask AI to quiz you on the names of anatomical structures, the directional terms (anterior, dorsal, ventral), or how homologous structures repeat across species until you can recite them without it. | Submitting AI text as your own lab notebook. The notebook is a record of what you observed and drew at the tray. Borrowed words describing a specimen you never opened are a falsified record. |
| Re-explaining hard concepts — have AI explain a frog-to-human forelimb comparison or what homology means, then check its claims against a labeled anatomy reference. A perch fin does not show the full tetrapod limb pattern. | Having AI describe structures you didn’t observe. Asking AI what a specimen “should” look like inside and copying that in, without opening, observing, and drawing the specimen yourself. |
| Checking your own work after you’ve done it — draw and label a structure yourself, then ask AI whether your labels and directional terms make sense. | Copying an identification without verifying. Pasting an AI label onto a structure you never located on your own specimen — it often names organs that aren’t even in that animal. |
| Summarizing your own notes — paste in your notes on dissection technique and ask AI to summarize, then check whether the summary matches what you meant. | Outsourcing the observation. Asking AI for the conclusion of an identification you were assigned to make yourself at the tray. |
| Debugging your reasoning — describe how you’d make a first incision step by step and ask AI where the technique would go wrong, then judge whether it’s right. | Disguising the source. Editing AI output just enough to hide where it came from, then presenting it as your own observation. |
After using AI, put the response aside and try to explain or complete the task yourself. If you cannot, you need more practice or instruction. That difficulty is not itself dishonesty. Presenting someone else’s reasoning or invented observations as your own is a different matter.
Why demonstrations matter
The instructor observes practical work, checks the student's own record, and asks follow-up questions. AI can support conceptual rehearsal but cannot certify what the learner actually did. A polished response or a timed conversation alone does not prove independent work.
Practice with AI; assess the student's science and practical evidence with a person.
Curated prompt library
Use an adult-approved tool and follow its age and account rules. Supply the context below before choosing a prompt. These are practice tasks, not permission to upload private records or obtain help on restricted assessments.
You are my Dissections practice coach, not my assessor. Unit and learning target: [teacher-approved unit and target] Level and prerequisites: [foundation / high-school / appropriate advanced work] Approved reference: [authorized excerpt, figure, key, or dataset with its source and page] Available units: 01 · Tools, Safety & the Ethics of Dissection; 02 · The Earthworm; 03 · The Grasshopper; 04 · The Clam or Squid; 05 · The Perch; 06 · The Frog; 07 · The Fetal Pig; 08 · Comparative Anatomy & the Dissection Defense Ask one question at a time and wait for my attempt. Do not assume my answer is wrong. Check feedback against the approved reference. Accept supported correct reasoning. If information is missing or inconsistent, ask for clarification; do not invent facts or citations. Give one targeted hint before a retry. Do not write my assessed response, supply invented observations, or treat a fluent answer as proof of mastery. Use de-identified or clearly labeled synthetic practice data. Do not request names, contact details, identifying images, family traits, or private health records. Use an instructor-approved procedure for conceptual rehearsal only. Do not suggest or approve hazardous procedures, changed reagents, cultures, incisions, diagnoses, or personal diet and exercise targets. Finish with the target practiced, evidence of understanding, what remains unverified, and one next practice step. Only the instructor assesses practical work and awards mastery.
Put the response aside and explain the idea independently. An incorrect answer is a learning signal, not dishonesty. Use the instructor's actual rubric for assessment, and verify any important AI suggestion before relying on it.
Checking the machine
Check AI responses. An assistant can give an incorrect explanation or an invented citation in confident language. Ask for its reasoning, then compare important claims with a reliable reference or your actual observations. A confident tone is not evidence of accuracy.
So treat every AI claim as a hypothesis, not a verdict. When the machine gives you an answer, do three things: check it against the specimen in front of you, confirm the directional terms, and never — ever — record an identification you haven’t located with your own eyes at the tray.
- Check what you actually found. Before trusting any label, ask what structure on your specimen justifies it. AI names organs that aren’t there.
- Redo the orientation. Confirm the directional terms — dorsal, ventral, anterior, posterior — against the specimen by hand. A confident label on the wrong side is a wrong label.
- Never take an identification on faith. If AI names a structure, confirm it against your own opened specimen and a reference before you write it down. This is the one place a check is non-negotiable.
A student who leaves this course able to catch the machine has learned something more durable than any single unit of dissection: how to think clearly in a world full of fluent, fast, confident voices that are sometimes simply wrong. That’s AI literacy. And it’s why we teach the tool instead of banning it.